We call for original and unpublished papers, which must be formatted in the standard IEEE two-column format that is used by the INFOCOM 2026 main conference, and must not exceed six pages in length (including references). All submitted papers will go through a strict peer review process, and all accepted papers that are presented by one of the authors at the workshop will be published in the IEEE INFOCOM 2026 workshop proceedings and IEEE Xplore.
Please submit your papers in PDF format via edas (link to be provided).
Submission Deadline: December 29, 2025
Notification of Acceptance: February 2, 2026
Camera Ready: February 16, 2026
Workshop: May 18, 2026
Deep learning has transformed many areas including the wireless domain. It has significantly unlocked the performance of wireless physical layer design, wireless sensing and wireless security. This workshop aims to bring together practitioners and researchers from both academia and industry for discussion and technical presentations on fundamental and practically relevant questions related to many challenges arising from deep learning for wireless communications, sensing and security. It also aims to provide the industry with fresh insight into the development of deep learning applications in wireless communication and networks.
In line with such objectives, original contributions, for both technical and demo sessions, are solicited on topics of interest to include, but not limited to, the following:
Deep learning for signal detection
Deep learning for channel modeling, estimation and prediction
Deep learning for resource optimization
Deep learning-based signal classification (including technology classification and modulation recognition)
Deep learning-based wireless sensing (including WiFi, mmWave radar, LoRa, RFID, etc)
Deep learning for localization and positioning
Deep learning for wireless security
Deep learning-based radio frequency fingerprint identification
Deep learning for physical layer security
Deep learning for wireless traffic analysis
Generative Models (e.g., LLM, Diffusion Models) for Wireless Data Synthesis
Large Language Models and Multi-modal Large Models for Wireless Communications, Sensing, and Security
Federated Learning for Wireless Communications, Sensing, and Security
AI-driven Digital Twins for Wireless Communications, Sensing, and Security
Explainable artificial intelligence for deep learning-based wireless communications, sensing, and security
Deep learning for emerging communication applications including intelligent reflection surface, unmanned aerial vehicles
Deep learning for new Internet of things applications
Adversarial attacks on deep learning-based wireless communication, sensing, and security
Professor Shiwen Mao, Auburn University, USA, smao@auburn.edu
Professor Yingying Chen, Rutgers University, USA, yingche@scarletmail.rutgers.edu
Professor Carlo Fischione, KTH Royal Institute of Technology, Sweden, carlofi@kth.se
Professor Jie Xu, The Chinese University of Hong Kong, Shenzhen, China. xujie@cuhk.edu.cn
Dr. Junqing Zhang, University of Liverpool, UK, junqing.zhang@liverpool.ac.uk
Dr. Xuyu Wang, Florida International University, USA, xuywang@fiu.edu
Dr. Francesca Meneghello, , Northwestern University, USA. fr.meneghello@northeastern.edu
to be updated
to be updated